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Expanding the Universe of Retinal Disease Gene Interactions Network Database, 2019, 1–11 doi: 10.1093/database/baz120 Original article Original article RPGeNet v2.0: expanding the universe of retinal disease gene interactions network Rodrigo Arenas-Galnares1,2,‡, Sergio Castillo-Lara1,2,‡, Vasileios Toulis1,2,3, Daniel Boloc4, Roser Gonzàlez-Duarte5, Gemma Marfany1,2,3,5,†,* and Josep F. Abril1,2,†,* 1Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, 08028, Catalo- nia, Spain, 2Institute of Biomedicine (IBUB), University of Barcelona, Barcelona, 08028, Catalonia, Spain, 3CIBERER, ISCIII, University of Barcelona, Barcelona, 08028, Catalonia, Spain, 4Faculty of Medicine, University of Barcelona, Barcelona, 08036, Catalonia, Spain and 5DBGen Ocular Genomics, Barcelona, 08028, Catalonia, Spain *Corresponding author. Tel: +34 93 403 1305; Email: [email protected] †The authors wish it to be known that last two authors should be regarded as joint senior authors. ‡The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint first authors. Citation details: Arenas-Galnares,R., Castillo-Lara,S., Toulis,V. et al. RPGeNet v2.0: expanding the universe of retinal disease gene interactions network. Database (2019) Vol. 2019: article ID baz120; doi:10.1093/database/baz120 Received 6 March 2019; Revised 9 September 2019; Accepted 13 September 2019 Abstract RPGeNet offers researchers a user-friendly queriable tool to visualize the interactome net- work of visual disorder genes, thus enabling the identification of new potential causative genes and the assignment of novel candidates to specific retinal or cellular pathways. This can be highly relevant for clinical applications as retinal dystrophies affect 1:3000 people worldwide, and the causative genes are still unknown for 30% of the patients. RPGeNet is a refined interaction network interface that limits its skeleton network to the shortest paths between each and every known causative gene of inherited syndromic and non-syndromic retinal dystrophies. RPGeNet integrates interaction information from STRING, BioGRID and PPaxe, along with retina-specific expression data and associated genetic variants, over a Cytoscape.js web interface. For the new version, RPGeNet v2.0, the database engine was migrated to Neo4j graph database manager, which speeds up the initial queries and can handle whole interactome data for new ways to query the network. Further, user facilities have been introduced as the capability of saving and restoring a researcher customized network layout or as novel features to facilitate navigation and data projection on the network explorer interface. Responsiveness has been further improved by transferring some functionality to the client side. Introduction genes (1). The prevalence of IRDs is 1:3000 worldwide, Inherited retinal dystrophies (IRDs) comprise a highly het- which make these blinding disorders a health relevant tar- erogeneous group of disorders caused by over 200 causative get. The implementation of massive sequencing approaches © The Author(s) 2019. Published by Oxford University Press. Page 1 of 11 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. (page number not for citation purposes) Page2of11 Database, Vol. 2019, Article ID baz120 has greatly facilitated genetic testing and, as a result, the number of IRD genes and mutations is constantly increas- ing. Nonetheless, a substantial number of cases remain to be accurately diagnosed, as the average yield in IRD genetic diagnosis is roughly 50%. Besides technical limitations, one of the bottlenecks in massive sequence-based molecular diagnosis is that most identified variants are previously unreported missense changes of unknown pathogenicity, either in known causative genes or in previously unreported candidates. These variants may be deemed as pathogenic or probably damaging by in silico predictive programs but end up classified as genetic variants of unknown significance, since there are not functional analyses to support their pathogenicity and their relationship to retinal physiology is yet to be determined. Molecular medicine based on gene and protein networks is rapidly expanding since most disease-causing genes often work together, either forming a protein complex or par- ticipating in the same signalling pathways. In contrast to the analysis of isolated genes, finding the networks that link disease candidate genes provides supporting data for the identification of new causative genes, functional clues for assaying putative pathogenic novel variants, as well as opens new scenarios to identify key therapeutic targets. Comprehensive tools to navigate through gene func- tions, cellular pathways and pathogenicity begin to emerge, particularly for cancer research. Although a considerable amount of genetic and functional data on IRDs genes and mutations has been gathered, there are not many Figure 1. Data integration workflow to build the RPGeNet core database. user-friendly searchable tools to make a network map of The final graphical web interface (bottom panels) depends on a series of data integration steps that provide the interactions and nodes to gene/proteins interactions. To fill this gap, a web applica- the main neo4j database engine. Each component of the workflow is tion, RPGeNet (2), was implemented that integrated all the described in detail on the main text. physical and genetic interactions at that time for a subset of IRD genes (retinitis pigmentosa and Leber congenital on the so-called skeleton network). The reduced network amaurosis), obtained from different databases and includ- allows users to better identify genes interconnecting drivers ing additional data such as tissue-specific expression. The that can be directly associated with retinal dystrophies expansion of genetic data, plus all the interactome and by searching through the shortest paths in the skeleton other omics information gathered of late, has prompted us graph (see workflow schema on Figure 1), instead of the to review and expand the initial 100 genes to more than immense number of interactions found in the complete 200 retinal dystrophy genes, to include distilled interaction whole network. RPGeNet does, however, allow users to databases and to implement an improved network genera- expand beyond the skeleton subnetwork if needed. To make tion, management, and visualization interface. searches more practical, RPGeNet expands the network by levels until recreating the entire known interactions graph (whole graph). Each level is an expansion in the parents and Materials and Methods children of the nodes in the previous level (see Figure 2), and RPGeNet is a tool to assist in the search for potential its newly added nodes tend to be either less significant to the candidate genes and pathways associated with retinitis disease the higher the level you go or poorly studied genes pigmentosa and intended to be used by both research with few known interactions. and genetic diagnostic purposes. RPGeNet refines the vast RPGeNet now has three distinct types of queries avail- interaction network by reducing it to the shortest paths able to undergo such disease-specific pathways characteri- between known driver genes of the disease (from now zation. The first query, as in the original RPGeNet, ‘is what Database, Vol. 2019, Article ID baz120 Page 3 of 11 Figure 2. Visual representation of the expansion of the core interaction graph. The graph builder begins with the construction of the skeleton graph, represented by all the nodes on the leftmost panel. The skeleton is created by finding the shortest direct interaction path (the red arrows) between all the known driver genes—drawn here using the same shapes as in RPGeNet Network Explorer (star, square and diamond shapes, based on whether their mutations cause syndromic diseases or not). The graph is then expanded into level one (represented as the orange panel) by adding all of the parents and children (straight lines) of the nodes already found in the skeleton graph. Level-specific interactions are shown as curved connections linking nodes within a given graph level. The expansion is repeated until the highest level is reached and all known genes with known interactions have been connected to the interactions core graph. The remaining genes that do not have any known interaction that connects them with the core graph are included in the whole graph level. Some of those genes may have interactions with other genes found only in the whole graph level (and many of those interactions are self-references). The bottom table from this figure compares the number of nodes and edges added on each level (‘New Nodes’ and ‘New Edges’ rows), as well as the total number of nodes and edges accumulated. interacts directly with the gene of interest?’ The user sets to characterize the shortest paths from a gene of interest, one or more gene of interest, and the output is a graph with not yet related to a disease, to one of the network levels the gene or genes of interest connected with all the genes already defined over the driver genes. The new database that interact directly. This graph can then be expanded, and engine facilitates that kind of query on the whole graph of its layout can be manually curated (Figure 3). The second interactions with respect to any of the nodes on the skeleton query is ‘what are all the shortest paths between two genes path and upper levels. Like in the previous query, a user can of interest?’ Two genes are provided, and a list of all the further explore a retrieved pathway from results listing on pathways between those two genes is returned (Figure 4). Network Explorer. However, as edges in the pathway are directional, only pathways driving from the first requested gene to the second Changes to database one are listed. To find possible pathways in the opposite direction, if any, the user should redo the query just switch- Driver genes ing the order of the query genes.
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